NETS 7332 — Machine Learning with Graphs
4 semester hoursGraduateLectureusually offered: falltypical days: T/FBostonTraditional
Covers a number of advanced topics in machine learning and data mining on graphs, including vertex classification, graph clustering, link prediction and analysis, graph distance functions, graph embedding and representation learning, deep learning for graphs, anomaly detection, graph summarization, network inference, adversarial learning on networks, and notions of fairness in social networks. Seeks to familiarize students with state-of-the-art descriptive and predictive algorithms on graphs. Requires a foundational understanding of calculus and linear algebra, probability, machine learning or data mining, algorithms, and programming skills.
Prerequisites
- PHYS 5116 (min C)
Offering history
| Term | Sections | Enrolled | Capacity | Full | Open seats/section |
|---|---|---|---|---|---|
| Fall 2023 | 1 | 9 | 10 | 90% | 1.0 |
| Fall 2024 | 1 | 6 | 10 | 60% | 4.0 |
| Fall 2025 | 1 | 8 | 10 | 80% | 2.0 |
Snapshots from scheduled scrapes — not live seat availability. "Full" can exceed 100% when sections over-enroll.
Meeting times
Share of recent sections by weekday: M 0% · T 100% · W 0% · Th 0% · F 100%
Common patterns: TF (100% of sections) — in patterns, R means Thursday
Professors
Fall
- Tina Eliassi-Rad (100% of students) · reviews
Percentages are each professor's average share of the season's enrolled students in recent terms.
Links
Official catalog (NETS course descriptions) · Student reviews on RateMyHusky · All NETS courses · Plan it at numap.app